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Semi-Supervised Learning on Meta Structure: Multi-Task Tagging and Parsing in Low-Resource Scenarios
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In: Conference of the Association for the Advancement of Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-02895835 ; Conference of the Association for the Advancement of Artificial Intelligence, Association for the Advancement of Artificial Intelligence, Feb 2020, New York, United States ; https://aaai.org/Conferences/AAAI-20/ (2020)
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Harnessing Code Switching to Transcend the Linguistic Barrier ...
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Improving Candidate Generation for Low-resource Cross-lingual Entity Linking
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In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 109-124 (2020) (2020)
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Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework ...
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Learning Rhyming Constraints using Structured Adversaries ...
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Domain Adaptation of Neural Machine Translation by Lexicon Induction ...
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Active Semi-Supervised Learning for Improving Word Alignment ...
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Active Semi-Supervised Learning for Improving Word Alignment ...
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Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations ...
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An Efficient Interlingua Translation System for Multi-lingual Document Production ...
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ParaMor: Minimally-Supervised Induction of Paradigm Structure and Morphological Analysis ...
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ParaMor: Finding Paradigms across Morphology ...
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Abstract:
Our algorithm, ParaMor, fared well in Morpho Challenge 2007 (Kurimo et al., 2007), a peer operated competition pitting against one another algorithms designed to discover the morphological structure of natural languages from nothing more than raw text. ParaMor constructs sets of affixes closely mimicking the paradigms of a language, and, with these structures in hand, annotates word forms with morpheme boundaries. Of the four language tracks in Morpho Challenge 2007, we entered ParaMor in English and German. Morpho Challenge 2007 evaluated systems on their precision, recall, and balanced F 1 at identifying morphological processes, whether those processes mark derivational morphology or inflectional features. In English, ParaMor’s balanced precision and recall outperform at F 1 an already sophisticated baseline induction algorithm, Morfessor (Creutz, 2006). ParaMor placed fourth in English overall. In German, ParaMor suffers from a low morpheme recall. But combining ParaMor’s analyses with analyses from ...
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Keyword:
89999 Information and Computing Sciences not elsewhere classified; FOS Computer and information sciences
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URL: https://dx.doi.org/10.1184/r1/6608282 https://figshare.com/articles/ParaMor_Finding_Paradigms_across_Morphology/6608282
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Neural Cross-Lingual Named Entity Recognition with Minimal Resources ...
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Zero-shot Neural Transfer for Cross-lingual Entity Linking ...
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